Improving Accuracy of an Artificial Neural Network Model to Predict Effort and Errors in Embedded Software Development Projects

被引：8
|
作者：
Iwata, Kazunori [1 ]
Nakashima, Toyoshiro [2 ]
Anan, Yoshiyuki [3 ]
Ishii, Naohiro [4 ]
机构：
[1] Aichi Univ, Dept Business Adm, 370 Shimizu,Kurozasa Cho, Aichi 4700296, Japan
[2] Sugiyama Jogakuen Univ, Dept Culture Informat Studies, Chikusa Ku, Nagoya, Aichi 4648662, Japan
[3] Omron Software Co Ltd, Shimogyo Ku, Kyoto 6008234, Japan
[4] Aichi Inst Technol, Dept Informat Sci, Toyota, Aichi 4700392, Japan
来源：
SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL-DISTRIBUTED COMPUTING 2010 | 2010年 / 295卷
关键词：
D O I：
10.1007/978-3-642-13265-0_2
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we propose a method for reducing the margin of error in effort and error prediction models for embedded software development projects using artificial neural networks(ANNs). In addition, we perform an evaluation experiment that uses Welch's t-test to compare the accuracy of the proposed ANN method with that of our original ANN model. The results show that the proposed ANN model is more accurate than the original one in predicting the number of errors for new projects, since the means of the errors in the proposed ANN are statistically significantly lower.
引用
收藏
页码：11 / +
页数：2
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